Vessel Detection From Nighttime Remote Sensing Imagery Based on Deep Learning

Shao, Jiangnan; Yang, Qingyao; Luo, Changyu; Li, Ronghao; Zhou, Yongsheng; Zhang, Feixiang

doi:10.1109/jstars.2021.3125834

Cited by 24 publications

(7 citation statements)

References 28 publications

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“…That is, the shallow feature map in the backbone module of Figure 1 b is integrated into the neck module of Figure 1 c. Kim et al [ 14 ] proposed the structure of ECAP-YOLO to improve the detection performance of small targets in aerial photography scenes. Shao et al [ 15 ] proposed an adaptive spatial feature fusion network with a high-resolution detection layer to enhance the effect of ship detection in night remote sensing scenes.…”

Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

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Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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“…However, due to the limitation of SAR image resolution, small vessel targets are much smaller in size than most optical images, and are susceptible to interference from clutter and some near-shore strong scatterers, so this structure is not very suitable for small vessel detection in SAR images. For small vessel detection, some scholars [9], [47] have improved the feature fusion part. As shown in Fig.…”

Section: A S-lpanmentioning

confidence: 99%

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

Zhou

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized and the sea-land segmentation process to remove land false alarms is time-consuming. Regarding these problems, firstly, a single low-level path aggregation network (S-LPAN) is designed specifically for small target. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Secondly, adaptive dual-polarimetric feature fusion (ADPFF) is proposed to filter the multi-channel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Thirdly, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset (P-DSD) containing small vessel detection and sea-land segmentation labels is created with reference to the LS-SSDD v1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.

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“…Han et al [29] optimized the backbone network of YOLO V4 and designed an amplified feeling field module for small-target detection for improving the acquisition of spatial small-target ship information and the robustness of the spatial displacement. Jiangnan et al [31] proposed an improvement based on YOLOv5. Tasff-yolov5 achieved a better feature fusion by fusing a tiny target detection layer and a four-layer adaptive spatial feature fusion network.…”

Section: Related Workmentioning

confidence: 99%